Systems and methods for performing truncation artifact correction

ABSTRACT

A method for performing truncation artifact correction includes acquiring a projection dataset of a patient, the projection dataset including measured data and truncated data, generating an initial estimate of a boundary between the measured data and the truncated data, using the measured data to revise the initial estimate of the boundary, estimating the truncated data using the revised estimate of the boundary, and using the measured data and the estimated truncated data to generate an image of the patient.

BACKGROUND OF THE INVENTION

The subject matter disclosed herein relates generally to imagingsystems, and more particularly, to systems and methods for performingtruncation artifact correction.

Computed Tomography (CT) imaging systems typically include an x-raysource and a detector. In operation, the x-rays are transmitted from thex-ray source, through a patient, and impinge upon the detector. Theinformation from the detector, also referred to herein as the measureddata, is then utilized to reconstruct a diagnostic image of the patient.However, under some scanning conditions, portions of the patient mayextend beyond a region measured by the detector, e.g. when the patientis larger than the scan field of view (SFOV) of the detector. The SFOVis defined as the region for which the patient will be fully measured bythe detector in every view. Additionally, the patient may not beproperly aligned with the detector. Imaging patients that are largerthan SFOV and/or patients that are improperly aligned with the detectormay result in image artifacts.

More specifically, the CT imaging system is utilized to reconstructcross-sectional images of the patient using a plurality of lineintegrals of the linear attenuation coefficients, e.g. the measureddata. However, when the patient extends beyond the SFOV of the detectoror the patient is improperly aligned with the detector, the lineintegrals outside the SFOV, also referred to herein as truncated data,are not known. Typically, the truncated data is therefore set to zero.Image reconstruction is then performed using the measured data and thetruncated data. However, the truncated data may result in imageartifacts, also referred to herein as truncation artifacts, in thereconstructed images. The truncation artifacts are typically visualizedon the reconstructed images as a bright ring at the edge of the detectorSFOV.

One known method of reducing truncation artifacts is to set thetruncated data to a value other than zero in a technique known aspadding. However, while padding may reduce the brightness of the ring atthe edge of the detector SFOV, padding still does not provide a veryaccurate representation of the truncated data outside the detector SFOV.

BRIEF DESCRIPTION OF THE INVENTION

In one embodiment, a method is provided for performing truncationartifact correction. The method includes acquiring a projection datasetof a patient, the projection dataset including measured data andtruncated data, generating an initial estimate of a boundary using themeasured data and the truncated data, using the measured data to revisethe initial estimate of the boundary, estimating the truncated datausing the revised estimate of the boundary, and using the measured dataand the estimated truncated data to generate an image of the patient.

In another embodiment, a non-transitory computer readable medium isprovided. The non-transitory computer readable medium being programmedto instruct a computer to acquire a projection dataset of a patient, theprojection dataset including measured data and truncated data, generatean initial estimate of a boundary using the measured data and thetruncated data, use the measured data to revise the initial estimate ofthe boundary, estimate the truncated data using the revised estimate ofthe boundary, and use the measured data and the estimated truncated datato generate an image of the patient.

In a further embodiment, an imaging system is provided. The imagingsystem includes a detector and a computer coupled to the detector. Thecomputer is configured to acquire a projection dataset of a patient, theprojection dataset including measured data and truncated data, generatean initial estimate of a boundary using the measured data and thetruncated data, use the measured data to revise the initial estimate ofthe boundary, estimate the truncated data using the revised estimate ofthe boundary, and use the measured data and the estimated truncated datato generate an image of the patient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B is a flowchart of an exemplary method for reconstructingan image of an object in accordance with various embodiments.

FIG. 2 is a simplified illustration of an exemplary imaging systemformed in accordance with various embodiments.

FIG. 3 is an example of truncation artifacts.

FIG. 4 is an example of truncation artifacts corrected by padding.

FIG. 5 is an unfiltered backprojection that may be generated using themethod shown in FIGS. 1A and 1B to dilate or erode the object support.

FIG. 6 is an error sinogram that may be generated using the method shownin FIGS. 1A and 1B.

FIG. 7 is the error sinogram masked to show only values at the objectboundary, which may be used to erode or dilate the boundary.

FIG. 8 is a pictorial view of an exemplary multi-modality imaging systemformed in accordance with various embodiments.

FIG. 9 is a block schematic diagram of the system illustrated in FIG. 8.

DETAILED DESCRIPTION OF THE INVENTION

The foregoing summary, as well as the following detailed description ofvarious embodiments, will be better understood when read in conjunctionwith the appended drawings. To the extent that the figures illustratediagrams of the functional blocks of the various embodiments, thefunctional blocks are not necessarily indicative of the division betweenhardware circuitry. Thus, for example, one or more of the functionalblocks (e.g., processors or memories) may be implemented in a singlepiece of hardware (e.g., a general purpose signal processor or a blockof random access memory, hard disk, or the like) or multiple pieces ofhardware. Similarly, the programs may be stand alone programs, may beincorporated as subroutines in an operating system, may be functions inan installed software package, and the like. It should be understoodthat the various embodiments are not limited to the arrangements andinstrumentality shown in the drawings.

Described herein are various embodiments for performing truncationartifact correction using an iterative method or algorithm. In variousembodiments, the method includes generating an initial estimate of aboundary that is between the measured data and the truncated data, e.g.a line between data that lies within a scan field of view (SFOV) anddata that lies outside the SFOV. Thus, when the patient image issegmented, the area within the boundary and representing the patient isinitially classified as water and the area outside the boundary isinitially classified as air. Thus, the boundary defines where thepatient goes from water to air. The iterative method then refines thereconstructed image by improving the estimate of the boundary. The massoutside the SFOV is first assumed to be water. At each iterative step inthe method, the reconstruction outside the detector SFOV is thresholdedinto water and air. The measured x-ray beams are compared to the x-raybeams acquired during a forward projection of the image which has beenthresholded outside the SFOV, and the changes are used to dilate orerode the estimate of the patient boundary. More specifically, aftereach thresholding iteration, the method performs a forward projectionand subtracts the data with the measured data. A difference sinogram isthen zeroed where no information is measured because of truncationartifacts. An unfiltered backprojection is then applied. Optionally, afiltered backprojection may also be utilized. A resulting differenceimage along the boundary may be thresholded. More specifically, wherethe boundary is above the threshold, the boundary is dilated. Where theboundary is below the threshold, the boundary is dilated. In variousembodiments, a full difference sinogram may not be estimated. Instead,at points along the object boundary, the difference between the measuredrays and the forward projected rays may be used to determine whether theboundary at the point should be dilated, eroded, or kept constant.

FIGS. 1A and 1B is a flowchart of an exemplary method 100 for performingtruncation artifact correction. Although the method 100 is described ina medical setting using a Computed Tomography (CT) imaging system, it iscontemplated that the benefits of the various embodiments describedherein accrue to all CT imaging systems including industrial CT imagingsystems such as, for example, a baggage scanning CT system typicallyused in a transportation center such as, for example, but not limitedto, an airport or a rail station.

At 102, the method includes acquiring a set of projection data. Invarious embodiments, the projection data may be acquired using anexemplary imaging system, such as a CT imaging system 150 shown in FIG.2. In various embodiments, the CT imaging system 150 includes an x-raysource 152 that projects a fan-shaped beam 154 which is collimated tolie within an X-Y plane of a Cartesian coordinate system and generallyreferred to as an “imaging plane”. The fan-shaped beam 154 includes aplurality of x-ray beams 156 that define the SFOV 158 of a detector 160.

In operation, the x-ray beams 156 passes through an object being imaged,such as a patient 162. The x-ray beams 156, after being attenuated bythe patient 162, impinge upon the detector 160. In various embodiments,the detector 160 includes a plurality of detector elements 164. Theintensity of the attenuated x-ray beams 156 received at each of thedetector elements 164 is dependent upon the attenuation of the x-raybeams 156 by the patient 162. More specifically, each detector element164 produces an electrical signal that represents the intensity of animpinging x-ray beam 156 and hence allows estimation of the attenuationof the x-ray beam 156 as the x-ray beam 156 passes through the patient162. In various embodiments, the detector 160 is a multislice detector160 that includes a plurality of parallel detector rows (not shown) ofdetector elements 164 such that projection data corresponding to aplurality of slices may be acquired simultaneously during a scan.

A group of x-ray attenuation measurements, i.e., projection data 180,from the detector 160 at one gantry angle is referred to as a “view”. A“scan” of the patient 162 may include a set of views made at differentgantry angles, or view angles, during one revolution of the x-ray source152 and the detector 160. The projection data 180 is then processed togenerate an image that corresponds to a two dimensional slice takenthrough the patient 162.

In various embodiments, a portion 164 the patient 162 may extend beyondthe SFOV 158 measured by detector 160 as shown in FIG. 2. Morespecifically, for the CT imaging system 150 to reconstruct images of thepatient 162, the patient 162 should be located within the SFOV 158 ofthe detector 160. Traditional reconstruction methods require all nonzeroline measurements to be known for accurate reconstruction to bepossible. As a result, the failure to collect attenuation informationconcerning portions of the patient 162 extending beyond the detectorSFOV 158 results in truncated views which result in truncationartifacts.

For example, FIG. 3 is an exemplary image 200 that is generated inaccordance with various embodiments. As shown in FIG. 3, the image 200is generated using non-truncated views 202 or images that are generatedusing the measured data, or non-truncated data. Measured data as usedherein is data acquired by the detector 160 and that lies within thedetector SFOV 158. Additionally, the image 200 includes truncated views204 that cause truncation artifacts 206 which in the illustratedembodiment, appear as a bright white line near a left side of the image200. Truncated views, or truncated data, as used herein refers to datathat lie outside the detector SFOV 158, or views which contain sometruncated data. Truncated views or data may also refer to data that iswithin the SFOV 158 but proximate to an edge of the detector 160, forexample data acquired by the reference channels 170 and/or 172.Accordingly, and referring again to FIGS. 1A and 1B, at 102 a projectiondataset that includes measured, or non-truncated data, and non-measured,or truncated data is acquired of the patient 162.

At 104, an initial estimate of the truncated data within the projectiondataset 180 is estimated to identify the truncated data. In variousembodiments, the truncated data may be estimated, or extrapolated, usingfor example, a padding method. In operation, the padding methodidentifies the last value measured in a specific detector channel andthen assigns the last value to the data to the truncated data outsidethe same detector channel. For example, and referring again to FIG. 2,assume that the last value acquired from a detector element 182 is one.Accordingly, the value one is assigned to all truncated data that liesin the same detector row, so that the value in missing data is the samevalue as that of detector element 182. It should be realized that thedetector 160 may include a plurality of detector rows. Accordingly, thepadding value assigned to each truncated data point is based on thespecific detector row of the detector 160. Accordingly, truncated dataalong different detector rows may be assigned the same value, or adifferent value.

In another embodiment, the truncated data may be modeled or estimated,on a view-by view basis, using a method referred to herein as watercylinder extrapolation. In operation, projections from neighboringchannels are utilized to perform the water cylinder extrapolation. Morespecifically, because the human anatomy typically does not changequickly over a small distance, e.g. a few millimeters, the measurementsalong a boundary 184 (shown in FIG. 2) also typically do not varysignificantly. Based on the boundary and the slope of the projectionmeasurements obtained at the edge of the detector, a location and a sizeof a cylindrical water object that can be best fitted to the truncatedprojection is generated. In operation, a size and location of the watercylinder is therefore estimated based on the weighted average describedabove. The water cylinder information may then be utilized as anestimate for the truncated data. More specifically, the truncated datais modeled on a view-by-view basis as a cylinder made of water bycalculating the slope and offset of the measured data at the boundary182 of the truncation in order to uniquely determine the size andlocation of the imaginary water cylinder. The projections through thewater cylinder may then be utilized to estimate the truncated data. Invarious embodiments, the water cylinder may be resealed or stretched toensure that the total mass of the water cylinder is consistentthroughout. In various other embodiments, a symmetric mirroring methodor a polynomial extrapolation method may be utilized to estimate thetruncated data. One method for using the water cylinder extrapolationtechnique is described in U.S. Pat. No. 6,856,666.

Referring again to FIGS. 1A and 1B, at 106 the measured data and theestimated truncated data are combined to generate a revised dataset.

At 108, the revised dataset is reconstructed to generate at least oneimage of the patient 162. For example, FIG. 4 is an exemplary image 220that may be reconstructed after implementing the water cylinderextrapolation technique described above. In various embodiments, therevised dataset may be reconstructed using a filtered backprojectiontechnique. In operation, the filtered backprojection technique convertsthe attenuation measurements from the scan information intoreconstructions of the object, typically in units called “CT numbers” or“Hounsfield units”, which are used to control the brightness of acorresponding pixel on a display. Optionally, the revised dataset may bereconstructed using any known reconstruction method.

At 110, and in various embodiments, the reconstructed image generated at108 is segmented as either water or air. More specifically, theHounsfield units derived at 108 are utilized to classify or segment thetruncated data as either water or air to provide an initial estimate ofa location of the boundary 184. For example, assume that a single pixelrepresenting a single truncated data point was previously estimated tohave a Hounsfield unit value of X. Accordingly, in various embodiments,if X is greater than a predetermined value, the single truncated datapoint is classified as water. Moreover, if X is less than thepredetermined value, the single truncated data point is classified asair. Accordingly, each of the truncated data points is classified aseither water or air based on the Hounsfield value assigned at 108. Inoperation, classifying the truncated data as either water or airfacilitates reducing errors that may result in shading artifacts, andmay occur as a result of implementing, for example, the water cylinderextrapolation technique described above. In various other embodiments,the truncated data may be classified into more than two groups. Forexample, the truncated data may be classified as water, air, bone,metal, or iodine. In order to improve the quality of the boundary,postprocessing steps may be used after the initial segmentation. Forexample, on the segmented image, binary image closing or opening may beused to produce a cleaner boundary between air and water.

Referring again to FIGS. 1A and 1B, at 112 the measured data and thesegmented truncated data, i.e the reconstruction, generated at 110 areforward projected to estimate the x-ray beams that were measured, e.g.the non-truncated data. In operation, forward projecting the projectiondata provides an estimate of what the measurements should be if thesegmentation into water and air were correct. Because the segmentationoutside the SFOV 158 is in general not correct, error exists between theforward projected measurements and the originally measured data. Withinthe SFOV 158, the water cylinder extrapolated result is generally good.However, as the data further outside the SFOV is extrapolated, thereconstructed image may exhibit increased quantities of artifacts.Moreover, the water cylinder extrapolated image is consistent with theoriginal measured data. Accordingly, when the reconstructed image isforward projected, a resulting sinogram, such as the sinogram 240 shownin FIG. 5, matches the measured data within the SFOV 158, but it is notconsistent with our prior knowledge of what images should look like. Forexample, the reconstructed sinogram 240 may include negative values,streaks, and/or artifacts 241.

Accordingly, and referring again to FIGS. 1A and 1B, at 114 the initialboundary estimate is modified, as described in more detail below, and anerror sinogram 244 is generated as shown in FIG. 7. In general, thesinogram 240 as described above, may be further refined or improved byiteratively thresholding or segmenting the truncated data into water andair as described above at 110. More specifically, when for example, thewater cylinder extrapolation technique is utilized, the resultantreconstructed image or sinogram 240 accurately represents theinformation acquired during the scan within the SFOV 158. Accordingly,while various techniques, such as the water cylinder extrapolationtechnique, facilitate providing an improved image, further improvementmay be desired to more accurately represent the truncated data.

In various embodiments, the initial boundary estimate may be modified orrevised on a per data point basis, e.g. modifying each truncated datapoint, which is a measurement of a line integral. For example, asdescribed above, the truncated data was initially estimated using thewater cylinder extrapolation. A resultant image was then reconstructedas shown in FIG. 4 and segmented into water and air. Accordingly, at114, the forward projected ray is compared to the measured ray. In oneembodiment, if the forward projected ray is larger than the measuredray, there is too much mass along that ray. Accordingly, the boundary184 is made smaller, or shrunk (eroded). If the forward projected ray issmaller than the measured ray, there is too little mass along that ray,the boundary 184 is expanded or dilated. Optionally, the boundary 184 isreduced or contracted and some of the water is reclassified as air.Repeating step 112 over all the rays yields an image that is moreconsistent with the measured data.

In various embodiments, step 114 may be implemented in sinogram space.For example, FIG. 6 is an error sinogram 242 generated using thedifference of the measured data and the forward projected data. FIG. 5,the label 240 identifies the unfiltered backprojection of the errorsinogram 242. Accordingly, in various embodiments, the initial boundaryestimate may be generated using the error or difference sinogram 242,which is segmented to show only the boundary as shown in FIG. 7. Thisimage 244 may then be thresholded and used to guide the reclassificationof boundary pixels from water into air or from air into water.

Referring again to FIGS. 1A and 1B, at 116 an unfiltered backprojectionis performed using the error sinogram 244 to dilate or erode the initialboundary estimate. In operation, the unfiltered backprojection enablesthe method to identify a pixel in the image and determine the effects ofthe reconstruction using the measured rays compared to the forwardprojected rays. To reduce the computation time of the unfilteredbackprojection, a subset of the data may be backprojected (for example,every other view, or every other ray within a view). A filter could beapplied if desired.

At 118, steps 110-116 are iteratively repeated until changes or movementof the boundary 184 is less than a predetermined threshold. For example,if a final iteration does not appreciably expand or contract theboundary 184, the iterative process may be completed and the finalboundary set at the location determined by the last location identifiedat step 116.

At 120, a forward projection is performed to generate the missing ortruncated data. More specifically, as described above, a forwardprojection was utilized for the measured data. At 120, data acquired at118 is forward projected to generate the missing or truncated data. Inthe exemplary embodiment, the truncated data is forward projected in theimage domain to provide an estimate for the measurements that areoutside the SFOV 158.

At 122, the measured data and the estimated data from the final forwardprojection at 120 are combined to generate a final or completeprojection dataset. In various embodiments, a blending or smoothingoperation may be performed on the final sinogram to facilitate reducingand/or eliminating discontinuities between the measured rays and theforward projected rays. At 124, the final projection dataset is utilizedto reconstruct a final image of the patient 162 using any suitablemethod. In various embodiments, the method 100 may also includesegmenting the image in an area outside the SFOV 158 to bone or iodineor iodine and water. For each bone, or iodine or metal location, themethod may include estimating an expected point spread function at eachlocation and then deconvolving the image by the point spread function toreduce artifacts caused by the bone or metal. This deconvolution couldbe regularized for stability, e.g. with a Wiener deconvolution.

The methods and algorithms described herein are used to performtruncation artifact correction. The methods and algorithms may beembodied as a set of instructions that are stored on a computer andimplemented using, for example, a module 330, shown in FIG. 8, software,hardware, a combination thereof, and/or a tangible non-transitorycomputer readable medium. In one embodiment, a tangible non-transitorycomputer readable medium excludes signals.

FIG. 8 is a pictorial view of an exemplary imaging system 300 that isformed in accordance with various embodiments. FIG. 9 is a blockschematic diagram of a portion of the multi-modality imaging system 300shown in FIG. 8. The imaging system may be embodied as a computedtomography (CT) imaging system, a positron emission tomography (PET)imaging system, a magnetic resonance imaging (MRI) system, an ultrasoundimaging system, an x-ray imaging system, a single photon emissioncomputed tomography (SPECT) imaging system, an interventional C-Armtomography imaging system, a CT system for a dedicated purpose such asextremity or breast scanning, and combinations thereof, among others. Inthe exemplary embodiment, the method 100 is described with respect to aCT imaging system.

Although various embodiments are described in the context of anexemplary dual modality imaging system that includes a computedtomography (CT) imaging system and a positron emission tomography (PET)imaging system, it should be understood that other imaging systemscapable of performing the functions described herein are contemplated asbeing used. Moreover, the various methods described herein may beimplemented with a stand-alone CT imaging system.

The multi-modality imaging system 300 is illustrated, and includes a CTimaging system 302 and a PET imaging system 304. The imaging system 300allows for multiple scans in different modalities to facilitate anincreased diagnostic capability over single modality systems. In oneembodiment, the exemplary multi-modality imaging system 300 is a CT/PETimaging system 300. Optionally, modalities other than CT and PET areemployed with the imaging system 300. For example, the imaging system300 may be a standalone CT imaging system, a standalone PET imagingsystem, a magnetic resonance imaging (MRI) system, an ultrasound imagingsystem, an x-ray imaging system, and/or a single photon emissioncomputed tomography (SPECT) imaging system, interventional C-Armtomography, CT systems for a dedicated purpose such as extremity orbreast scanning, and combinations thereof, among others.

The CT imaging system 302 includes a gantry 310 that has an x-ray source312 that projects a beam of x-rays toward a detector array 314 on theopposite side of the gantry 310. The detector array 314 includes aplurality of detector elements 316 that are arranged in rows andchannels that together sense the projected x-rays that pass through anobject, such as the subject 306. The imaging system 300 also includes acomputer 320 that receives the projection data from the detector array314 and processes the projection data to reconstruct an image of thesubject 306. In operation, operator supplied commands and parameters areused by the computer 320 to provide control signals and information toreposition a motorized table 322. More specifically, the motorized table322 is utilized to move the subject 306 into and out of the gantry 310.Particularly, the table 322 moves at least a portion of the subject 306through a gantry opening 324 that extends through the gantry 310.

The imaging system 300 also includes a module 330 that is configured toimplement various methods and algorithms described herein. The module330 may be implemented as a piece of hardware that is installed in thecomputer 320. Optionally, the module 330 may be implemented as a set ofinstructions that are installed on the computer 320. The set ofinstructions may be stand alone programs, may be incorporated assubroutines in an operating system installed on the computer 320, may befunctions in an installed software package on the computer 320, and thelike. It should be understood that the various embodiments are notlimited to the arrangements and instrumentality shown in the drawings.

As discussed above, the detector 314 includes a plurality of detectorelements 316. Each detector element 316 produces an electrical signal,or output, that represents the intensity of an impinging x-ray beam andhence allows estimation of the attenuation of the beam as it passesthrough the subject 306. During a scan to acquire the x-ray projectiondata, the gantry 310 and the components mounted thereon rotate about acenter of rotation 340. FIG. 9 shows only a single row of detectorelements 316 (i.e., a detector row). However, the multislice detectorarray 314 includes a plurality of parallel detector rows of detectorelements 316 such that projection data corresponding to a plurality ofslices can be acquired simultaneously during a scan.

Rotation of the gantry 310 and the operation of the x-ray source 312 aregoverned by a control mechanism 342. The control mechanism 342 includesan x-ray controller 344 that provides power and timing signals to thex-ray source 312 and a gantry motor controller 346 that controls therotational speed and position of the gantry 310. A data acquisitionsystem (DAS) 348 in the control mechanism 342 samples analog data fromdetector elements 316 and converts the data to digital signals forsubsequent processing. For example, the subsequent processing mayinclude utilizing the module 330 to implement the various methodsdescribed herein. An image reconstructor 350 receives the sampled anddigitized x-ray data from the DAS 348 and performs high-speed imagereconstruction. The reconstructed images are input to the computer 320that stores the image in a storage device 352. Optionally, the computer320 may receive the sampled and digitized x-ray data from the DAS 348and perform various methods described herein using the module 330. Thecomputer 320 also receives commands and scanning parameters from anoperator via a console 360 that has a keyboard. An associated visualdisplay unit 362 allows the operator to observe the reconstructed imageand other data from computer.

The operator supplied commands and parameters are used by the computer320 to provide control signals and information to the DAS 348, the x-raycontroller 344 and the gantry motor controller 346. In addition, thecomputer 320 operates a table motor controller 364 that controls themotorized table 322 to position the subject 306 in the gantry 310.Particularly, the table 322 moves at least a portion of the subject 306through the gantry opening 324 as shown in FIG. 8.

Referring again to FIG. 9, in one embodiment, the computer 320 includesa device 370, for example, a floppy disk drive, CD-ROM drive, DVD drive,magnetic optical disk (MOD) device, or any other digital deviceincluding a network connecting device such as an Ethernet device forreading instructions and/or data from a computer-readable medium 372,such as a CD-ROM, a DVD or an other digital source such as a network orthe Internet, as well as yet to be developed digital means. In anotherembodiment, the computer 320 executes instructions stored in firmware(not shown). The computer 320 is programmed to perform functionsdescribed herein, and as used herein, the term computer is not limitedto just those integrated circuits referred to in the art as computers,but broadly refers to computers, processors, microcontrollers,microcomputers, programmable logic controllers, application specificintegrated circuits, and other programmable circuits, and these termsare used interchangeably herein.

In the exemplary embodiment, the x-ray source 312 and the detector array314 are rotated with the gantry 310 within the imaging plane and aroundthe subject 306 to be imaged such that the angle at which an x-ray beam374 intersects the subject 306 constantly changes. A group of x-rayattenuation measurements, i.e., projection data, from the detector array314 at one gantry angle is referred to as a “view”. A “scan” of thesubject 306 comprises a set of views made at different gantry angles, orview angles, during one revolution of the x-ray source 312 and thedetector 314. In a CT scan, the projection data is processed toreconstruct an image that corresponds to a two dimensional slice takenthrough the subject 306.

Exemplary embodiments of a multi-modality imaging system are describedabove in detail. The multi-modality imaging system componentsillustrated are not limited to the specific embodiments describedherein, but rather, components of each multi-modality imaging system maybe utilized independently and separately from other components describedherein. For example, the multi-modality imaging system componentsdescribed above may also be used in combination with other imagingsystems.

It should be noted that the various embodiments may be implemented inhardware, software or a combination thereof. The various embodimentsand/or components, for example, the modules, or components andcontrollers therein, also may be implemented as part of one or morecomputers or processors. The computer or processor may include acomputing device, an input device, a display unit and an interface, forexample, for accessing the Internet. The computer or processor mayinclude a microprocessor. The microprocessor may be connected to acommunication bus. The computer or processor may also include a memory.The memory may include Random Access Memory (RAM) and Read Only Memory(ROM). The computer or processor further may include a storage device,which may be a hard disk drive or a removable storage drive such as asolid state drive, optical drive, and/or the like. The storage devicemay also be other similar means for loading computer programs or otherinstructions into the computer or processor.

As used herein, the term “computer” may include any processor-based ormicroprocessor-based system including systems using microcontrollers,reduced instruction set computers (RISC), application specificintegrated circuits (ASICs), logic circuits, GPUs, FPGAs, and any othercircuit or processor capable of executing the functions describedherein. The above examples are exemplary only, and are thus not intendedto limit in any way the definition and/or meaning of the term“computer”. The computer or processor executes a set of instructionsthat are stored in one or more storage elements, in order to processinput data. The storage elements may also store data or otherinformation as desired or needed. The storage element may be in the formof an information source or a physical memory element within aprocessing machine.

The set of instructions may include various commands that instruct thecomputer or processor as a processing machine to perform specificoperations such as the methods and processes of the various embodimentsof the invention. The set of instructions may be in the form of asoftware program. The software may be in various forms such as systemsoftware or application software, which may be a non-transitory computerreadable medium. Further, the software may be in the form of acollection of separate programs, a program module within a largerprogram or a portion of a program module. The software also may includemodular programming in the form of object-oriented programming. Theprocessing of input data by the processing machine may be in response touser commands, or in response to results of previous processing, or inresponse to a request made by another processing machine.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralof said elements or steps, unless such exclusion is explicitly stated.Furthermore, references to “one embodiment” of the present invention arenot intended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features. Moreover, unlessexplicitly stated to the contrary, embodiments “comprising” or “having”an element or a plurality of elements having a particular property mayinclude additional elements not having that property.

Also as used herein, the phrase “reconstructing an image” is notintended to exclude embodiments of the present invention in which datarepresenting an image is generated, but a viewable image is not.Therefore, as used herein the term “image” broadly refers to bothviewable images and data representing a viewable image. However, manyembodiments generate, or are configured to generate, at least oneviewable image.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by acomputer, including RAM memory, ROM memory, EPROM memory, EEPROM memory,and non-volatile RAM (NVRAM) memory. The above memory types areexemplary only, and are thus not limiting as to the types of memoryusable for storage of a computer program.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the inventionwithout departing from its scope. While the dimensions and types ofmaterials described herein are intended to define the parameters of theinvention, they are by no means limiting and are exemplary embodiments.Many other embodiments will be apparent to those of skill in the artupon reviewing the above description. The scope of the invention should,therefore, be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled. Inthe appended claims, the terms “including” and “in which” are used asthe plain-English equivalents of the respective terms “comprising” and“wherein.” Moreover, in the following claims, the terms “first,”“second,” and “third,” etc. are used merely as labels, and are notintended to impose numerical requirements on their objects. Further, thelimitations of the following claims are not written inmeans-plus-function format and are not intended to be interpreted basedon 35 U.S.C. §112, sixth paragraph, unless and until such claimlimitations expressly use the phrase “means for” followed by a statementof function void of further structure.

This written description uses examples to disclose the variousembodiments, including the best mode, and also to enable any personskilled in the art to practice the various embodiments, including makingand using any devices or systems and performing any incorporatedmethods. The patentable scope of the various embodiments is defined bythe claims, and may include other examples that occur to those skilledin the art. Such other examples are intended to be within the scope ofthe claims if the examples have structural elements that do not differfrom the literal language of the claims, or if the examples includeequivalent structural elements with insubstantial differences from theliteral languages of the claims.

What is claimed is:
 1. A method for performing truncation artifactcorrection, said method comprising: acquiring a projection dataset of apatient, the projection dataset including measured data and truncateddata; combining forward projected truncated data and the measured datato generate a revised dataset; reconstructing at least one image usingthe revised dataset; classifying the reconstruction into either water orair to generate generating an initial estimate of a location of aboundary between the patient and air using the measured data and thetruncated data; using the measured data to revise the initial estimateof the location of the boundary; estimating the truncated data using therevised estimate of the location of the boundary; and using the measureddata and the estimated truncated data to generate an image of thepatient.
 2. The method of claim 1, wherein generating the initialestimate of the location of the boundary includes generating the initialestimate of the location of the boundary using at least one of a watercylinder extrapolation technique, a symmetric mirroring technique or apolynomial extrapolation technique.
 3. The method of claim 1, furthercomprising comparing the measured data to the forward projection of theclassified image.
 4. The method of claim 3, further comprising: dilatingor eroding the initial boundary estimate to generate an error sinogram;and performing a backprojection on the error sinogram.
 5. The method ofclaim 1, further comprising iteratively estimating the truncated datausing the revised estimate of the location of the boundary until theboundary converges.
 6. The method of claim 1, wherein using the measureddata further comprises: forward projecting the revised estimate togenerate a revised set of truncated data; combining the forwardprojected truncated data and the measured data to generate a final setof projection data; and utilizing the final set of projection data togenerate the image of the patient.
 7. The method of claim 1, whereinusing the measured data further comprises: segmenting the image in anarea outside a scanned field of view to bone, iodine, or metal andwater; for each bone, iodine or metal location, estimating an expectedpoint spread function of bone at each location; and deconvolving thepoint spread function to reduce artifacts.
 8. A non-transitory computerreadable medium being programmed to instruct a computer to: acquire aprojection dataset of a patient, the projection dataset includingmeasured data and truncated data; combine forward projected truncateddata and the measured data to generate a revised dataset; reconstruct atleast one image using the revised dataset; classify the reconstructioninto either water or air to generate an initial estimate of a locationof a boundary between the patient and air using the measured data andthe truncated data; use the measured data to revise the initial estimateof the location of the boundary; estimate the truncated data using therevised estimate of the location of the boundary; and use the measureddata and the estimated truncated data to generate an image of thepatient.
 9. The non-transitory computer readable medium of claim 8,wherein the computer is further instructed to generate the initialestimate of the location of the boundary using a water cylinderextrapolation.
 10. The non-transitory computer readable medium of claim8, wherein the computer is further instructed to: generate an errorsinogram using the measured data and a forward projection of thereconstruction, and using the error sinogram to dilate or erode theinitial boundary estimate.
 11. The non-transitory computer readablemedium of claim 8, wherein the computer is further instructed toiteratively estimate the location of the boundary using the measureddata until the boundary converges.
 12. The non-transitory computerreadable medium of claim 8, wherein the computer is further instructedto: perform a forward projection using the revised estimate of thelocation of the boundary to generate a revised estimate of the truncateddata; combine the forward projected truncated data and the measured datato generate a final set of projection data; and utilize the final set ofprojection data to generate the image of the patient.
 13. Thenon-transitory computer readable medium of claim 8, wherein the computeris further instructed to: segment the image in an area outside a scannedfield of view to bone, iodine, or metal and water; for each bone, iodineor metal location, estimate an expected point spread function of bone,iodine, or metal at each location; and deconvolve the point spreadfunction to reduce artifacts.
 14. An imaging system comprising: adetector; an x-ray source configured to transmit a plurality of x-raysthrough a patient to the detector; a computer coupled to the detector,the computer configured to: acquire a projection dataset of a patient,the projection dataset including measured data and truncated data;combine forward projected truncated data and the measured data togenerate a revised dataset; reconstruct at least one image using therevised dataset; classify the reconstruction into either water or air togenerate an initial estimate of a location of a boundary between thepatient and air using the measured data and the truncated data; use themeasured data to revise the initial estimate of the location of theboundary; estimate the truncated data using the revised estimate of thelocation of the boundary; and use the measured data and the estimatedtruncated data to generate an image of the patient.
 15. The imagingsystem of claim 14, wherein the computer is further configured togenerate the initial estimate of the location of the boundary using awater cylinder extrapolation.
 16. The imaging system of claim 14,wherein the computer is further configured to: generate an errorsinogram using the measured data and forward projection of thereconstruction, and use the error sinogram to use it to dilate or erodethe initial boundary estimate.
 17. The imaging system of claim 14,wherein the computer is further configured to iteratively estimate thetruncated data using the revised estimate of the location of theboundary until the boundary converges.